Perspectives on `What makes a good theory?'
(Iris van Rooij and Chris Donkin)

Psychological science has been going through some turbulent times due to the 'replication
crisis' and the various reforms it has motivated. While reforms so far seem to have focused
on experimental and statistical practices, some mathematical psychologists and computational
modelers have raised concerns that such reforms, when generally enforced, obstruct rather
than foster scientific progress. There seems to be growing consensus that improving
psychological science necessitates improving its theoretical practices as well. There is
unclarity, however, about what this entails and how it may be achieved. With this symposium
we aim to contribute to clarifying the issue.

The symposium brings together a diverse group of mathematical psychologists and computational
cognitive scientists and invites them to give their perspective on the question 'what makes a
good theory?'. Each speaker is asked to reflect not only on the characteristics of 'good
theories' but also on how on their view such theories may be identified and/or generated. The
symposium's explicit aim is to foster dialogue on the complexities and nuances of the question
posed and to create a platform for a pluralism of epistemological perspectives.

Mathematical psychology has embraced a sophisticated analytical toolbox for formulating, fitting,
and evaluating cognitive models. In contrast, when designing experiments, similarly sophisticated
quantitative methods are often ignored in favor of heuristics, intuitions, and design-by-convention.
The field of optimal experimental design (OED) aims to rectify this asymmetry by treating the
problem of designing informative experiments just as rigorously as the problem of designing
cognitive models. The conceptual foundations of OED were already established in statistics by the
1950s, but progress has been impeded by the computational difficulty of the arising optimization
problems. Nevertheless, with recent methodological developments and increasing computing power,
researchers are now identifying many ways in which OED can benefit science.

This symposium will provide an opportunity for mathematical psychologists to learn about recent
developments and applications of OED in cognitive modeling. Some of the recent methodological
developments include hierarchical adaptive design optimization (ADO), variational Bayesian OED,
implementation of OED using probabilistic programming, and Gaussian process optimization of
experimental designs. Moreover, OED methods are now being applied across a diverse set of
psychological domains, such as perception, learning, decision making, and even in complex research
settings such as neuroimaging. With this symposium, we hope to expose the community to the variety
of recent developments in OED and to encourage further contributions to this emerging research
program.

Computational model-based cognitive neuroscience
(Percy Mistry)

To better understand human behavior, the field of model-based cognitive neuroscience seeks to
anchor psychological theory to the biological substrate from which behavior originates: the brain.
Despite complex dynamics, fluctuations in brain activity have been related to fluctuations in
components of cognitive models, which instantiate psychological theories. In this symposium, we
will discuss recent advances in: (a) Statistical frameworks for joint modeling of behavioral and
neuroimaging data, by exploiting patterns of covariation between the streams of data; (b) Combining
hierarchical latent-mixture and evidence accumulation-based models with fMRI data within a Bayesian
inference framework, to improve neuro-cognitive process dissociation, and improve the functional,
spatial, and temporal resolution of associating brain circuit features with distinct cognitive and
behavioral components of decision-making processes; (c) Multivariate dynamical systems approach for
simultaneous modeling of behavioral and neural measures to explicitly account for temporal dynamics
and brain functional connectivity; and (d) Hidden Markov model approaches and Bayesian methods to
investigate dynamic temporal properties of interactions between brain networks, including
characterization of cognitive state-switching.

The talks will include simulations and experimental data from a range of tasks including
perceptual, cognitive, mathematical, and consumer decision-making. They will address issues of
scalability, determining significance of brain-behavior connections, and linking spatio-temporal
dynamics. They will demonstrate how it is possible to improve differentiation between theories
using joint modeling approaches, and how identification of latent neuro-cognitive states can
improve specification of psychological theories. They will provide insights into the developmental
maturation of brain networks, and the role of differential brain feature characteristics in
clinical and neuro-diverse populations.